The present embodiments relate to medical diagnostic imaging or quantification. In particular, assessment of heart valves is performed from medical diagnostic imaging data.
Valvular surgery accounts for up to 20% of all cardiac procedures in the United States and is applied in nearly 100,000 patients every year. Yet, with an average cost of $120,000 and 5.6% in hospital death rate, valve operations are expensive and risky cardiac interventions. Aortic and mitral valves are most commonly affected, cumulating in 64% and 15%, respectively of all valvular heart disease (VHD) cases.
The heart valves play a key role in the cardiovascular system by regulating the blood flow inside the heart chambers and human body. In particular, the aortic and mitral valves execute synchronized rapid opening and closing movements to govern the fluid interaction in between the left atrium (LA), left ventricle (LV) and aorta (Ao).
Congenital, degenerative, structural, infective or inflammatory diseases can provoke dysfunctions, resulting in stenotic and regurgitant valves. The blood flow is obstructed or, in case of regurgitant valves, blood leaks due to improper closing. Both conditions may greatly interfere with the pumping function of the heart, causing life-threatening conditions. Severe cases require valve surgery, while mild to moderate cases benefit from accurate diagnosis and long-term medical management. Precise morphological and functional knowledge about the aortic-mitral apparatus is important for diagnosis, therapy-planning, surgery or percutaneous intervention as well as patient monitoring and follow-up.
Non-invasive investigations are based on two-dimensional images, user-dependent processing and manually performed, potentially inaccurate measurements. Imaging modalities, such as Cardiac Computed Tomography (CT) and Transesophageal Echocardiography (TEE), enable for dynamic four dimensional scans of the beating heart over the whole cardiac cycle. Such volumetric time-resolved data encodes comprehensive structural and dynamic information. However, the four dimensional scans are rarely exploited in clinical practice due to data size and complexity. Perceiving the valve operation is difficult.
Diagnosis may be assisted by modeling. Dynamic model estimation determines patient specific parameters from volume scan data. Modeling may be approached in two steps—object delineation and motion estimation.
For object delineation, approaches may be based on active shape models (ASM), active appearance models (AAM) or de-formable models. These methods often involve semi-automatic inference or require manual initialization for object location. Discriminative learning methods may efficiently solve localization problems by classifying image regions as containing a target object. This learning-based approach may be applied to three-dimensional object localization by introducing an efficient search method referred to as marginal space learning (MSL). To handle the large number of possible pose parameters of a 3D object, an exhaustive search of hypotheses is performed in sub-spaces with gradually increased dimensionality.
For motion estimation in time dependent four-dimensional problems, tracking methods have been used. To improve robustness, many tracking algorithms integrate key frame detection. The loose coupling between detector and tracker often outputs temporally inconsistent results.
Trajectory-based features have also increasingly attracted attention in motion analysis and recognition. The inherent representative power of both shape and trajectory projections of non-rigid motion are equal, but the representation in the trajectory space may significantly reduce the number of parameters to be optimized. This duality has been exploited in motion reconstruction and segmentation of structure from motion. In particular, for periodic motion, frequency domain analysis shows promising results in motion estimation and recognition.
The majority of cardiac models focus on the representation of the left (LV) and the right ventricle (RV). More comprehensive models include the left (LA) and right atrium (RA), ventricular outflow tracts (LVOT and RVOT), or the aorta (Ao) and pulmonary trunk (PA). Nevertheless, the models do not explicitly model the aortic or mitral valves. Existent valve models are mostly generic and used for hemodynamic studies or analysis of various prostheses rather than being patient specific. A model of the mitral valve used for manual segmentation of TEE data has been proposed. The model includes only the mitral valve annulus and closure line during systole, so is both static and simple. A representation of the aortic-mitral coupling has been proposed. This model is dynamic but limited to only a curvilinear representation of the aortic and mitral annuli. Due to the narrow level of detail and insufficient parameterization, none of the existent valve models are applicable for comprehensive patient-specific modeling or clinical assessment.